Adaptive intermolecular interaction parameters for accurate Mixture Density Functional Theory calculations

TL;DR

This paper introduces adaptive mixing rules for intermolecular parameters in mixture density functional theory, improving the accuracy of thermodynamic property predictions for fluid mixtures by fitting to experimental data.

Contribution

It presents a novel application of adaptive mixing rules to obtain intermolecular parameters for mixture DFT, enhancing model accuracy for thermodynamic predictions.

Findings

Mixture DFT EoS agrees well with experimental isotherms.

Predictions of vapor-liquid equilibrium data are less successful.

Adaptive mixing rules improve parameter fitting to experimental data.

Abstract

The description of fluid mixtures molecular behavior is significant for various industry fields due to the complex composition of fluid found in nature. Statistical mechanics approaches use intermolecular interaction potential to predict fluids behavior on the molecular scale. The paper provides a comparative analysis of mixing rules applications for obtaining intermolecular interaction parameters of mixture components. These parameters are involved in the density functional theory equation of state for mixtures (Mixture DFT EoS) and characterize thermodynamic mixture properties in the bulk. The paper demonstrates that Mixture DFT EoS with proper intermolecular parameters agrees well with experimental mixtures isotherms in bulk for different mixtures. However, predictions of vapor-liquid equilibrium (VLE) experimental data are not successful. Halgren HHG, Waldman - Hagler, and adaptive mixing rules that adjust on the experimental data from the literature are used for the first time to obtain intermolecular interaction parameters for the mixture DFT model. The results obtained provide a base for understanding how to validate the DFT fluid mixture model for calculating thermodynamic properties of fluid mixtures on a micro and macro scale.